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Feature Selection and Pedestrian Detection Based on Sparse Representation.

Shihong Yao1, Tao Wang1, Weiming Shen1

  • 1State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.

Plos One
|August 22, 2015
PubMed
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This summary is machine-generated.

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This study identifies sparse feature subsets for pedestrian detection, improving efficiency. Sparse Histogram of Oriented Gradients (HOG) and Local Shape (LSS) features offer robust description and faster processing.

Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Pedestrian detection is challenged by diverse and high-dimensional features.
  • Effective feature extraction is crucial for accurate pedestrian detection systems.

Purpose of the Study:

  • To investigate sparse feature subsets for improved pedestrian detection.
  • To evaluate the descriptive ability and stability of sparse feature subsets.
  • To enhance computational efficiency in pedestrian detection.

Main Methods:

  • Theoretical analysis and experimental comparison of six features: SIFT, SURF, Haar, HOG, LBP, and LSS.
  • Application of sparse representation to screen effective sparse feature subsets.
  • Fusion of feature pairs and sparse representation to identify key components.
  • Evaluation of sparse subsets for descriptive ability, stability, and computational speed.

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Main Results:

  • Sparse feature subsets retain significant descriptive components of original features.
  • Sparse Histogram of Oriented Gradients (HOG) and Local Shape (LSS) features demonstrate comparable descriptive ability to full features with reduced computation time.
  • HOG and LSS exhibit the highest ratio of sparse to full feature sets among the evaluated features.
  • Combined sparse HOG-LSS features show enhanced distinguishing ability and parsimony.

Conclusions:

  • Sparse representation is effective for reducing feature dimensionality in pedestrian detection.
  • HOG and LSS are optimal features for sparse subset selection in pedestrian detection.
  • The combination of sparse HOG-LSS features offers a promising approach for efficient and accurate pedestrian detection.